Volume 2, Issue 1 — Year 2025 — Article e100035

ISSN (Online): 3115-8129 Biannually

Landslide Inventory Database for Hazard Assessment in the Alborz Mountains, Iran

Article Type: Database
Pages: e100035
DOI: https://doi.org/10.22034/CGEL.2.1.e100035
Crossmark
Open Access
Authors
Affiliations
  1. Department of Geology, Islamic Azad University Science and Research Branch, Tehran 1477893855, Iran
  2. School of Civil Engineering, Henan Polytechnic Institute, Nanyang 473000, China
  3. School of Civil Engineering, Henan Polytechnic Institute, Nanyang 473000, China
  4. School of Information Engineering, Xinjiang Institute of Engineering, Xinjiang 830099, China
  5. School of Information Engineering, Xinjiang Institute of Engineering, Xinjiang 830099, China
Corresponding author
Email: zchen@163.com
Received: 25 February 2025 / Accepted: 17 May 2025 / Published: 25 May 2025
Abstract

This study presents a detailed inventory database of landslides in the Alborz Mountains, compiled through a combination of satellite imagery, historical records, aerial photographs, and field surveys. The database encompasses critical information on the location, type, size, mechanism, and triggering factors of landslides, alongside their environmental and societal impacts. Covering an area of approximately 2,500 km², the inventory documents 2,984 recorded landslides. To ensure accessibility and usability, the dataset is provided in a standardized format, complete with metadata and quality control details, facilitating its application and reuse by researchers and practitioners. This comprehensive resource is designed to support a wide range of applications, including landslide susceptibility mapping, hazard assessment, and land-use planning. The database serves as a crucial tool for improving the management of landslide risks in the Alborz Mountains and comparable regions. By offering a reliable and organized compilation of data, it aids in identifying vulnerable areas, assessing hazards, and guiding sustainable land-use strategies. Additionally, the dataset promotes collaboration and data sharing within the scientific community, enabling further research and development in landslide risk management. This initiative underscores the importance of data-driven approaches to natural hazard mitigation and highlights the potential societal benefits of open and reusable research data. The inventory represents a significant step toward enhancing our understanding of landslide dynamics and fostering resilience in regions prone to such geological hazards.

Keywords

Landslide inventory, Geohazards, Alborz mountains, hazard assessment, Susceptibility mapping

Data availability statement

The data supporting the findings of this study are available within article. No publicly archived dataset was generated.

Funding

This research received no external funding.

Highlights
  • Database documents 2984 landslides in Alborz
  • Data supports hazard mapping and risk assessment
  • Dataset enables planning and research collaboration
References
  1. Aghdam I.N., Varzandeh M.H.M., Pradhan B. (2016). Landslide susceptibility mapping using an ensemble statistical index (Wi) and adaptive neuro-fuzzy inference system (ANFIS) model at Alborz Mountains (Iran). Environmental Earth Sciences, 75(1), 1-20. https://doi.org/10.1007/s12665-015-4866-8.
  2. Alaminia Z., Tadayon M., Griffith E.M., Solé J., Corfu F. (2021). Tectonic-controlled sediment-hosted fluorite-barite deposits of the central Alpine-Himalayan segment, Komsheche, NE Isfahan, Central Iran. Chemical Geology, 566, 120084. https://doi.org/10.1016/j.chemgeo.2021.120084.
  3. Arabameri A., Pradhan B., Rezaei K., Lee C.W. (2019). Assessment of landslide susceptibility using statistical- and artificial intelligence-based FR–RF integrated model and multiresolution DEMs. Remote Sensing, 11(9), 999. https://doi.org/10.3390/rs11090999.
  4. Asadi S., Sharghi A., Atefi M. (2019). Zoning of physical-infrastructural vulnerabilities to landslides using GIS: Case study of settlements in Tehran and Alborz provinces. Disaster Prevention and Management Knowledge, 9(4), 329-340. https://dor.isc.ac/20.1001.1.23225955.1398.9.4.1.6.
  5. Azarafza M., Akgün H., Atkinson P.M., Derakhshani R. (2021). Deep learning-based landslide susceptibility mapping. Scientific Reports, 11(1), 24112. https://doi.org/10.1038/s41598-021-03585-1.
  6. Azarafza M., Ghazifard A., Akgün H., Asghari-Kaljahi E. (2018). Landslide susceptibility assessment of South Pars Special Zone, southwest Iran. Environmental Earth Sciences, 77, 805. https://doi.org/10.1007/s12665-018-7978-1.
  7. Chen W., Pourghasemi H.R., Kornejady A., Xie X. (2019). GIS-based landslide susceptibility evaluation using certainty factor and index of entropy ensembled with alternating decision tree models. In: Natural hazards GIS-based spatial modeling using data mining techniques, pp. 225-251. Springer.
  8. Daviran M., Shamekhi M., Ghezelbash R., Maghsoudi A. (2022). Landslide susceptibility prediction using artificial neural networks, SVMs, and random forest: Hyperparameters tuning by genetic optimization algorithm. International Journal of Environmental Science and Technology, 20(1), 259-276. https://doi.org/10.1007/s13762-021-03796-z.
  9. Farahi S., Ardalan A.A., Sheikhzakariayi S.J., Ansari M. (2020). Petrology and tectono-magmatic environment of the volcanic rocks of South Marzanabad, Central Alborz Mountain. Revista Geoaraguaia, 10(2), 28–50.
  10. Gaidzik K., Ramírez-Herrera M.T. (2021). The importance of input data on landslide susceptibility mapping. Scientific Reports, 11(1), 19334. https://doi.org/10.1038/s41598-021-98757-6.
  11. Ghassemi M.R. (2004). Geology and tectonics of the Alborz range and South Caspian Basin. In: Proceedings of the 32nd International Geological Congress, Florence, Italy.
  12. Ghorbani M. (2021). Geological setting and crustal structure of Iran. In: The geology of Iran: Tectonic, magmatism and metamorphism, pp. 1-22. Springer.
  13. Hussain M.A., Chen Z., Zheng Y., Shoaib M., Shah S.U., Ali N., Afzal Z. (2022). Landslide susceptibility mapping using machine learning algorithm validated by persistent scatterer In-SAR technique. Sensors, 22(9), 3119. https://doi.org/10.3390/s22093119.
  14. Karaman M.O., Çabuk S.N., Pekkan E. (2022). Utilization of frequency ratio method for the production of landslide susceptibility maps: Karaburun Peninsula case, Turkey. Environmental Science and Pollution Research, 29(60), 91285-91305. https://doi.org/10.1007/s11356-022-21931-2.
  15. Mao Y., Mwakapesa D.S., Wang G., Nanehkaran Y.A., Zhang M. (2021). Landslide susceptibility modelling based on AHC-OLID clustering algorithm. Advances in Space Research, 68(1), 301–316. https://doi.org/10.1016/j.asr.2021.03.029.
  16. Mohsen E.M. (2022). Properties of fault zones and their influences on rainfall-induced landslides: Examples from Alborz and Zagros ranges. Environmental Earth Sciences, 81(5), 168. https://doi.org/10.1007/s12665-022-10106-8.
  17. Mokhtari M., Abedian S. (2019). Spatial prediction of landslide susceptibility in Taleghan basin, Iran. Stochastic Environmental Research and Risk Assessment, 33, 1297-1325. https://doi.org/10.1007/s00477-019-01709-7.
  18. Ngo P.T.T., Panahi M., Khosravi K., Ghorbanzadeh O., Kariminejad N., Cerda A., Lee S. (2021). Evaluation of deep learning algorithms for national scale landslide susceptibility mapping of Iran. Geoscience Frontiers, 12(2), 505-519. https://doi.org/10.1016/j.gsf.2020.09.006.
  19. Nikoobakht S., Azarafza M., Akgün H., Derakhshani R. (2022). Landslide susceptibility assessment by using convolutional neural network. Applied Sciences, 12(12), 5992. https://doi.org/10.3390/app12125992.
  20. Pacheco Quevedo R., Velastegui-Montoya A., Montalván-Burbano N., Morante-Carballo F., Korup O., Daleles Rennó C. (2023). Land use and land cover as a conditioning factor in landslide susceptibility: A literature review. Landslides, 20(5), 967-982. https://doi.org/10.1007/s10346-023-01949-6.
  21. Rosser N., Kincey M., Oven K., Densmore A., Robinson T., Pujara D.S., Shrestha R., Smutny J., Gurung K.S., Lama S., Dhital M.R. (2021). Changing significance of landslide hazard and risk after the 2015 Mw 7.8 Gorkha, Nepal earthquake. Progress in Disaster Science, 10, 100159. https://doi.org/10.1016/j.pdisas.2021.100159.
  22. Saha S., Arabameri A., Saha A., Blaschke T., Ngo P.T.T., Nhu V.H., Band S.S. (2021). Prediction of landslide susceptibility in Rudraprayag, India, using novel ensemble of conditional probability and boosted regression tree-based on cross-validation method. Science of the Total Environment, 764, 142928. https://doi.org/10.1016/j.scitotenv.2020.142928.
  23. Shahri A.A., Spross J., Johansson F., Larsson S. (2019). Landslide susceptibility hazard map in southwest Sweden using artificial neural network. Catena, 183, 104225. https://doi.org/10.1016/j.catena.2019.104225.
  24. Shao X., Xu C. (2022). Earthquake-induced landslides susceptibility assessment: A review of the state-of-the-art. Natural Hazards Research, 2(3), 172–182. https://doi.org/10.1016/j.nhazr.2022.08.002.
  25. Sheikholeslami M.R. (2018). Tectonosedimentary evolution of the basins in Central Alborz, Iran. Geosciences, 106, 29-38.
  26. Shokri P., Dehbozorgi M., Hakimi Asiabar S. (2020). Recent tectonic activity in Central Alborz Mountain, Iran: Morphometric analysis and knickpoint identification. Journal of Mountain Science, 17(12), 3137–3161. https://doi.org/10.1007/s11629-020-6014-8.
  27. Wang H., Zhang L., Luo H., He J., Cheung R.W.M. (2021). AI-powered landslide susceptibility assessment in Hong Kong. Engineering Geology, 288, 106103. https://doi.org/10.1016/j.enggeo.2021.106103.
  28. Zhao P., Masoumi Z., Kalantari M., Aflaki M., Mansourian A. (2022). A GIS-based landslide susceptibility mapping and variable importance analysis using artificial intelligent training-based methods. Remote Sensing, 14(1), 211. https://doi.org/10.3390/rs14010211.
  29. Zhu A.X., Miao Y., Liu J., Bai S., Zeng C., Ma T., Hong H. (2019). A similarity-based approach to sampling absence data for landslide susceptibility mapping using data-driven methods. Catena, 183, 104188. https://doi.org/10.1016/j.catena.2019.104188.
How to cite
Yeganeh, A., Nazari, R., Zhu, D., Tagizadeh, P., & Chen, Z. (2025). Landslide Inventory Database for Hazard Assessment in the Alborz Mountains, Iran. Civil and Geoengineering Letters, 2(1), e100035. https://doi.org/10.22034/CGEL.2.1.e100035
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